首页|Hunan University Reports Findings in Machine Learning (Predicting the Fundraising Performance of Environmental Crowdfunding Projects: an Interpretable Machine Learning Approach)
Hunan University Reports Findings in Machine Learning (Predicting the Fundraising Performance of Environmental Crowdfunding Projects: an Interpretable Machine Learning Approach)
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Elsevier
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news report- ing originating from Changsha, People’s Republic of China, by NewsRx correspondents, research stated, “Crowdfunding has become a pivotal fundraising method for environmental organizations. How-ever, the fundraising performance of environmental crowdfunding projects remains subpar, prompting the need for improvements.” Funders for this research include National Natural Science Foundation of China (NSFC), Natural Science Foundation of Hunan Province. Our news editors obtained a quote from the research from Hunan University, “Effectively addressing this challenge entails the precise prediction of each project’s fundraising performance and a comprehensive understanding of the intricate correlations between various features and fundraising success. In response to these imperatives, this study introduces an interpretable framework meticulously designed for pre-dicting the fundraising performance of environmental crowdfunding projects. This comprehen-sive framework integrates ten theoretically significant features to form the predictive model’s feature set. It adopts a diverse array of eight algorithms for training and harnesses SHAP values and ALE plots for insightful post-hoc interpretation, thereby providing valuable insights into the nuanced roles played by these features. Validated on a dataset comprising 3,101 environmental crowdfunding projects from Tencent Charity, the proposed framework outperforms state-of-the -art methods, demonstrating an improvement of 5.9% in predictive performance. Furthermore, the post-hoc interpretation techniques accurately depict the roles of the features.”
ChangshaPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine LearningHunan University
NETL
NSTL
Elsevier
